自然资源遥感, 2023, 35(2): 1-15 doi: 10.6046/zrzyyg.2022145

综述

遥感技术在苹果园精准种植管理中的应用现状及展望

赵海岚,1,2, 蒙继华,1, 纪云鹏3

1.中国科学院空天信息创新研究院数字地球重点实验室,北京 100094

2.中国科学院大学,北京 100049

3.陕西果业集团有限公司,西安 710016

Application status and prospect of remote sensing technology in precise planting management of apple orchards

ZHAO Hailan,1,2, MENG Jihua,1, JI Yunpeng3

1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

3. Shaanxi Fruit Industry Group Company Limited, Xi’an 710016, China

通讯作者: 蒙继华(1977-),男,博士,研究员,博士生导师,主要从事农业遥感理论、方法与应用研究。Email:mengjh@aircas.ac.cn。

责任编辑: 陈理

收稿日期: 2022-04-14   修回日期: 2022-06-19  

基金资助: 国家自然科学基金面上项目“基于作物模型与遥感数据同化的农田土壤速效养分反演方法研究”(41871261)
中国科学院科技服务网络计划项目“智慧农业核心技术突破与集成示范”(KFJ-STS-ZDTP-057)

Received: 2022-04-14   Revised: 2022-06-19  

作者简介 About authors

赵海岚(1998-),男,硕士研究生,主要从事植被遥感监测研究。Email: zhaohailan20@mails.ucas.ac.cn。

摘要

在果园种植管理向精准化和数字化发展的趋势下,苹果栽培对果园种植管理支撑技术提出了更高的要求。近些年,遥感技术的空间分辨率和重访频率不断突破,已经成为苹果园精准种植管理的主要支撑技术,然而目前鲜有综述文章进行这方面的现状梳理和展望,因此对这类研究进行总结很有必要。通过分析遥感技术在苹果园精准种植管理中的主要应用情况,将遥感技术的应用领域归纳为果园基础信息调查、果林参数反演和果园种植管理支撑3大类,并综述遥感技术在各领域中的应用方法、效果,探讨应用潜力。最后,总结出当前研究和应用存在机理性研究少且部分应用领域研究不足、多技术集成化程度不高、缺乏大范围的示范应用3类问题,并指出苹果树生长模拟机理模型、一体化苹果种植管理支撑系统、基于卫星数据的单木监测、遥感监测产品多元服务4个研究主题将成为下一步的研究和应用热点。

关键词： 遥感; 苹果园; 精准农业; 果树监测

Abstract

With the trend towards the precise and digital planting management of orchards, apple cultivation relies more heavily on the planting management supporting technologies of orchards. In recent years, continuous breakthroughs made in spatial resolution and revisiting frequency have made remote sensing technology a major supporting technology for the precise planting management of apple orchards. However, there is an absence of reviews of the application status and prospect of this technology in the planting management of orchards. Based on the analysis of primary applications of remote sensing technology in the precise planting management of apple orchards, this study classified the applications into three major categories, namely the surveys of basic orchard information, inversions of orchard parameters, and the planting management support of orchards. Furthermore, this study reviewed the methods and performance of the applications of remote sensing technology in various fields and explored the application potential. Finally, it identified three types of problems with current research and application of remote sensing technology, namely insufficient studies on mechanisms and in some application fields, low-degree integration of multiple technologies, and the lack of large-scale application models. In addition, this study proposed four hot research and application topics in the future, namely models used to simulate the growth mechanisms of apple trees, the integrated support system for the planting management of apple trees, the single-tree monitoring based on satellite data, and the diversified services of remote sensing-based monitoring products.

Keywords： remote sensing; apple orchard; precision agriculture; fruit tree monitoring

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本文引用格式

赵海岚, 蒙继华, 纪云鹏. 遥感技术在苹果园精准种植管理中的应用现状及展望[J]. 自然资源遥感, 2023, 35(2): 1-15 doi:10.6046/zrzyyg.2022145

ZHAO Hailan, MENG Jihua, JI Yunpeng. Application status and prospect of remote sensing technology in precise planting management of apple orchards[J]. Remote Sensing for Land & Resources, 2023, 35(2): 1-15 doi:10.6046/zrzyyg.2022145

0 引言

苹果(Malus domestica L. Borkh)栽培是世界上重要的农业生产类型之一[1],截止2019年全世界共有90多个国家发展了苹果种植业,年产出苹果超过8 000万t[2]。中国是世界上最大的苹果生产国,苹果种植面积为204万hm2、年产量为4 243万hm2,2项数量均超过了世界总量的40%[2]。自2001年中国加入世界贸易组织后,中国苹果出口量排名世界第一[3],成为对外贸易中最具竞争力的农产品之一[4],2019年出口值为13亿美元[2]。除了生产和出口,中国还是苹果消费大国之一,拥有庞大的国内市场,主要分布在陕西、山东、甘肃、山西和河南等省的苹果种植业为当地带来了巨大的经济效益[3,5]。然而,由于种植结构不合理[5] 、果树老化[1]、水分胁迫[6]、养分应用不平衡[7]和病虫害[8]等问题,中国苹果种植业面临着单位面积产量低的困境,平均产量仅为20.79 t/hm2,位列世界第27,远远落后于苹果种植业发达的国家,如新西兰(56.72 t/hm2)、瑞士(50.78 t/hm2)、智利(50.09 t/hm2)等国[2]。受新冠疫情、国际局势和市场供需等因素的影响,中国苹果价格自2020年以来大幅下降,以年周期期货价为参考,2021年(6 309.48元/t)相比2019年(10 789.26元/t)的苹果单位价格下降了41.52%,给苹果种植业造成了巨大冲击[9]。

为了科学种植和培育果树,稳定和增加苹果产出,保障果园经济效益,管理者需要依靠一系列准确及时的果园环境、果树生理生化等信息作出栽种、灌溉、施肥、病虫害防治等决策。传统上,获取苹果种植信息主要通过站点观测[10]、种植记录[1]、人工取样[5]和实验室测样[11]等方法,这类方法耗时、费力,大规模应用的成本较高,不能满足果树的大面积、实时监测和快速管理[5,11-12]。遥感技术经过几十年的发展,获取数据的时间分辨率、空间分辨率、光谱分辨率和辐射分辨率取得了突破性进展,已成为快捷、高效、低成本果林识别监测和果园种植管理的新手段。

随着遥感与全球定位系统、地理信息系统、大数据分析和人工智能等新兴技术的深度结合并广泛实践于农业精准管理[13],农业运营持续优化、增加产量和减少投入、降低损失的目标正在逐步实现[14-15],其中一些技术已应用于苹果生产中[8,16],苹果种植也迈入了精准管理时代。本文围绕苹果园精准种植管理过程对遥感技术的应用需求,汇总和分类现有的相关报道,分析各类研究和应用解决的热点问题、实用性和不足,探讨相关技术的应用潜力并展望研究和应用趋势,为遥感技术更好地服务果园精准种植管理、保障苹果优产提供理论支撑。

1 遥感技术在苹果园精准种植管理中的应用现状及潜力

现代农业精准管理相关研究始于20世纪80年代末,遥感技术在早期精准管理中的主要应用是基于全色、多光谱、高光谱卫星和航空影像的单产估算,面向的主要对象是玉米、小麦和大豆等大田作物[15],针对果树的应用研究较少。随着遥感监测的高分辨率和低重访频率相关技术不断突破,不同国家学者根据他们的应用目的和技术条件开发出了基于植被指数、生理生化参数、环境指标的植被普适监测方法[17-18],这类方法也被应用于苹果树监测中[19-20],服务果园种植管理。近些年,优质量、高产量的苹果生产需求对果园种植管理提出了更高的标准,饶晓燕等[21]和Odi-Lara等[16]针对苹果园环境、果树生长状况和种植户要求等开发出了专用于苹果园的遥感精准服务技术。本节将从果园基础信息遥感调查、果林参数遥感反演和果园种植管理支撑3类遥感应用方向出发,总结遥感技术在苹果种植环节中的应用现状,分析当前研究和应用的不足,探讨相关遥感技术的应用潜力。

1.1 果园基础信息遥感调查

数字化生产管理是果园精准种植管理的发展趋势[22],管理者可通过获取果园数字化信息来掌握果园动态变化情况,及时作出果园种植管理决策,从而确保精准完成果园灌溉、机器收割、种植结构优化和生长监测等工作[1,23 -24]。在众多反映苹果园各方面动态变化的信息中,果林分布、果园林龄和果园基础设施分布是描述果园整体情况的本底信息,也是开展果园精准种植管理工作需要使用的基础信息,因此获取果园数字化信息的关键和前提是调查、掌握果林分布、果园林龄和果园基础设施分布情况。相比于传统的实地调查,遥感调查方法可显著提高调查效率,支撑果园基础数字化信息的快速获取。

1.1.1 果林分布调查

果林分布遥感调查的最终目标是为了尽可能准确地识别出遥感影像中唯一归属于苹果林的像元,这类像元包含的时间、空间和光谱信息,是后续进行果林一切遥感分析的基础,可直接应用于面积统计和灌溉施肥估算等工作。

当前,国内外农业遥感识别研究大多数关注于小麦、玉米和水稻等农作物[25],专门针对苹果林的遥感识别研究相对较少。现有的苹果林识别分类方法主要可分为混合像元分解法(decomposition of mixed pixels method,DMP)、支持向量机法(support vector machine method,SVM)、最大似然法(maximum likelihood method,ML)、决策树法(decision tree method,DT)等监督分类法和K-Means法等非监督分类法[5,26⇓⇓⇓-30]。这些方法围绕像元尺度、地块尺度和单木尺度开展[31⇓-33],其中像元尺度以苹果林(树)像元个体为最小分类单元; 地块和单木尺度一般都以多个像元组成的斑块为最小分类单元,两者之间的差异在于地块尺度的斑块一般由苹果树像元和其他地物像元组成,如树间裸土、杂草等,而单木尺度的识别分类对遥感影像的空间分辨率要求较高,其最小分类斑块一般仅包含单棵苹果树冠层范围内的像元。基于像元尺度的识别分类方法主要利用像元光谱信息,通过对比不同像元与苹果林(树)样点像元之间的光谱特征,将相似像元划归为苹果林(树)类别。Zhu等[5]根据苹果园12个月Sentinel-2和Landsat TM/ETM+/OLI的归一化植被指数(normalized difference vegetation index,NDVI)时间序列数据的变化特征和差异,利用SVM对像元进行分类,识别苹果林的精度超过98.90%; 辛群荣[28]根据花期苹果树的植被指数等信息结合当地土地利用类型分布状况调查结果,利用分类回归树(classification and regression tree,CART)算法进行GF-1影像决策树分类,识别苹果林的精度超过97%。这类方法的优势在于理论相对简单,但“同物异谱”和“同谱异物”现象会显著影响分类精度,忽略了苹果树冠层和聚类成林的纹理信息,分类结果存在“椒盐”现象。

基于地块或单木尺度的识别分类方法则可以合理利用高空间分辨率影像中包含的纹理信息,有效减少“椒盐”现象,并且分类结果一般具有良好的整体性[34]。该方法需要先将遥感影像分割成若干同质同性的斑块,然后划分相似特征信息的斑块为同一类[35],分类依据一般为斑块的光谱、形态、大小、空间关系等特征[36]。其中关键在于影像分割,主要基于2种思路[37]: ①二维影像分割,根据二维影像的光谱、纹理等特征,利用计算机图像处理技术对二维影像进行区域分割,常用的分割算法有基于目标对象的图像分析、基于边缘算法、数字表面模型(digital surface model,DSM)、冠层高度模型(canopy height model,CHM)的分割方法等[38⇓⇓-41]; ②三维点云分割,根据三维点云数据具有的空间结构关系和苹果树所具有的真实三维特征,制定多种空间点云分类规则,直接针对点云数据进行果树检测和树冠分割[37]。Wu等[42]采用U-Net深度学习网络,根据苹果树树冠形态和纹理分割果园影像中的每棵苹果树,利用剪枝策略从语义分割结果中提取果树凸出边界后自动计算树冠参数,获得总准确率超过92%的苹果树树冠参数; Sun等[43]利用无人机遥感获取果园三维点云模型和植被指数,建立基于植被指数的自动行、列检测方法实现快速分割,通过点云模型高精度识别苹果树。

随着遥感影像的种类不断增多,如何选用合适的数据和识别分类方法十分重要。现阶段,基于像元尺度和地块尺度[5,44]开展的苹果林识别分类工作多是利用2~30 m中高空间分辨率的卫星遥感数据,该尺度空间分辨率的果林识别结果可用于反演树木生理生化指标、冠层反射率[45]; 亚米级或更高空间分辨率遥感数据常用在基于单木尺度的识别分类中,该尺度空间分辨率的果林识别结果可用于果树单木提取和基于单木管理的果实计数、冠层建模和精准灌溉等工作[46-47]。

1.1.2 果园林龄调查

林龄调查是获取果树年龄并为果树更新提供决策参考的重要手段。对于果园投资者来说,苹果栽培是一项投入高、回报周期长的产业,以矮砧密植和乔砧密植2种栽培模式为例,在良好的管理条件下,矮砧密植果园一般于第2年挂果,第4年达到成龄丰产果园水平; 乔砧密植果园一般第4—5年才开始挂果,第8—10年才能达到丰产期,从开始挂果到丰产期之间的产量差别最高可达数万kg/hm2[48],并且果树老化会降低苹果产量和品质[1]。因此,苹果林龄对种植户更新树苗、调整种收计划和政府机构制定果林老化改造政策等工作都具有重要参考意义。

传统上,苹果林龄调查主要依靠果树种植记录或破坏性采样[1],这类方法虽然可以保证准确性,但很难应用于大规模调查。目前,从遥感数据中大范围、快速提取林龄的方法已有了相当数量的研究,但多是应用于森林调查[49],在苹果林龄调查中的应用极少,现有的苹果林龄研究主要采用经验统计方法,通过构建林龄与光谱特征之间的统计模型来反演实际光谱特征对应的苹果林龄,Zhu等[1]提出了一种区域尺度苹果林龄识别方法,该方法利用Sentinel-2影像和Landsat影像的NDVI时间序列,采用逐像素逆时间序列计算方法反演苹果林龄。这类方法考虑了苹果林龄增长与冠层光谱特征变化之间的规律,但仅依靠光谱信息无法减弱“同物异谱”和“同谱异物”现象对反演精度的影响。而苹果树在生长过程中除了冠层光谱特征发生变化,其形态、结构特征也会与之前产生差异,表现为果树冠层面积增加、郁闭度增大等,这些信息在当前苹果林龄遥感调查中尚缺乏利用,但其他植被的类似研究可以提供应用范例,主要分为2类: ①利用激光雷达点云数据的方法,即根据点云数据获取的苹果结构信息进行苹果林龄识别,Iizuka等[50]使用ALOS卫星的相控阵L波段合成孔径雷达(PALSAR)数据提取植被的结构信息,从而估算林龄; ②利用卫星影像结合激光雷达点云数据的方法,Rizeei等[51]提出了一种结合WorldView-3影像和机载激光雷达数据估算林龄的方法,该方法将SVM的4个核函数进行比较,得到最佳的树冠覆盖轮廓,并结合郁闭度和结构参数构建统计模型,实现林龄的准确估算。

由于苹果园果树一般排列规整、树间距明显,因此上述2类利用植被形态、结构信息的林龄估算方法在苹果园中将有较好的应用潜力。另外,利用遥感技术监测苹果林龄和产量并建立两者之间的统计模型,可为提前布局果林投资、制定供销计划提供优势信息。

1.1.3 果园基础设施分布调查

基础设施分布遥感调查的主要目的是为了摸清基础设施分布、数量等基本情况,掌握基础设施数字化信息并进行空间制图,为科学布置基础设施支撑果园种植管理提供本底资料。在苹果园中,服务苹果种植的基础设施主要为机耕道、沟渠和仓库,这些基础设施是机械林间作业、果园灌溉排水、肥料农药存储等环节的支撑和保障,其规划、利用和改造需要适应于果园精准种植管理的要求,达到节约集约的标准并实现高效服务的目标。随着精确灌溉、机器人收割、无人机喷洒等自动化、精准化农业措施越来越广泛地应用于果园[52⇓⇓⇓⇓⇓-58],基础设施数字化管理将成为苹果园精准种植管理的重要环节,果园基础设施分布调查是实现苹果园基础设施数字化管理的基础。

与果林分布调查原理相似,基础设施识别同样是利用遥感识别分类方法确定影像中唯一归属于目标地物的像元,但由于基础设施和果林存在光谱特征、几何纹理特征等本质上的影像特征差异,例如果园基础设施的光谱曲线随季节变化较小、具有明显的矩形形状等,因此进行果园基础设施分布调查时需充分考虑基础设施与其他地物之间的可分条件,找出适宜的分类依据和方法。然而,当前的基础设施识别相关研究绝大多数关注于城市、农村的主要设施,如道路[59-60]、房屋[61-62]、大棚[63-64]等,关注于苹果园基础设施遥感识别的研究鲜见,缺乏基础设施识别技术在苹果园中的应用范例。但遥感在其他类果园基础设施调查中的应用足以证明其拥有巨大潜力[65],陈蜀江等[66]利用Landsat7和Google Earth影像分析葡萄干晾房光谱、纹理特征,筛选不同光谱、纹理特征和其他参数进行2次分类,以88.6%的识别精度提取了研究区葡萄干晾房。将主流的设施遥感识别方法作为参考,有3类适用于苹果园基础设施分布调查: ①模板匹配法[67-68],根据苹果园模板基础设施特征设计模板规则,利用规则匹配目标地物; ②面向对象法[68-69],将影像分割成斑块,然后根据斑块内苹果园基础设施特征识别目标地物; ③知识驱动法[68,70 -71],生成与苹果园基础设施相关的知识模型进行目标识别。

从调查成果的用途来看,设施分布信息可作为基础数据绘制导航地图和障碍地图用于自动化机械的苹果林间作业,这类用途对影像数据的空间分辨要求会较高,可由无人机遥感提供支撑[72]; 除此之外,该信息也能作为空间数据绘制规划图用于基础设施的新建、改造、拆除等工作,此类用途可由米级或亚米级卫星遥感提供支持。与一般作物种植区相比,苹果园种植密度小、基础设施排列规整,地物之间交叉、遮挡的现象相对较少,因此基础设施的数字化信息较易通过遥感获取,使得遥感技术能更加精准、广泛地服务于苹果园基础设施数字化管理。

1.2 果林参数遥感反演

苹果树生长过程是一个生物生理生化反应响应环境变化的复杂过程,受光照、温度、水分、养分、田间管理等多种因素综合影响。传统的果林参数地面获取方式很难实时、快捷地反馈苹果林整体状态,还会造成树体损伤[73-74]。因此,利用遥感技术高效反演果林参数对于果园精准种植管理十分重要。当前,植被生理生化参数和环境参数的遥感反演技术已在苹果林监测中有了一些应用,涉及的生理生化参数主要为叶面积指数(leaf area index,LAI)[45,75]、叶绿素[11,76]和养分[77⇓-79]; 针对环境参数的研究较少,主要涉及冠层温度[80]。准确、及时的苹果林生理生化参数和果园环境参数信息为管理者获知果林生长状态、评估果树生理需求提供了关键保障。

1.2.1 果树生理生化参数反演

生理生化参数是果树生长状态的数字化描述,对果树长势、水分、养分、病虫害监测和估产等工作有重要意义[20,45,77,81]。由于遥感主要捕捉的是植被光谱信息,所以参数反演一般需要借助特定的植被指数或地面实测参数,利用经验统计模型或机理模型模拟植被特定时期的生理生化状况[77,82]。在苹果林理化参数遥感反演中,果树LAI、叶绿素含量和养分含量是最主要的反演对象,这3类指标在评估植被病虫害、营养状况和产量等方面发挥着重要作用[83]。

经验统计模型是当前最常用的反演方法,该方法通过构建光谱指数与苹果林(树)LAI、叶绿素含量和养分含量之间的回归关系,根据影像数据计算的苹果林(树)光谱指数值估算生理生化参数。其中,苹果林(树)LAI常用NDVI、土壤调整植被指数(soil adjusted vegetation index,SAVI)、比值植被指数(ratio vegetation index,RVI)、增强植被指数(enhanced vegetation index,EVI)、绿色归一化差异植被指数(green normalized difference vegetation index,GNDVI)5种植被指数进行反演[45,75,82],Liu等[75] 在0.01置信度水平下选取了5种与LAI相关性最高的植被指数(NDVI,RVI,EVI,SAVI和GNDVI)训练支持向量回归(support vector regression,SVR)和梯度提升决策树(gradient-boosting decision trees,GBDT)模型,利用该模型准确地预测了苹果林LAI。叶绿素是苹果树光合作用能力、营养胁迫和发育衰老各阶段的良好指示因子[84],常用NDVI和RVI等植被指数进行反演[11,76,85]。Li等[11] 利用Sentinel-2数据计算基于修订NDVI公式的新植被指数(NDVIgreen+NDVIred+NDVIre),发现基于该新植被指数的SVM模型反演苹果树冠层叶绿素含量的效果优于BP神经网络模型。果树养分常由NDVI、可见光大气阻抗指数(visible atmospherically resistant index,VARI)、RVI、标准叶绿素指数(normalized pigment chlorophyll index,NPCI)、EVI和氮反应指数(nitrogen response index,NRI)等植被指数反演[77⇓-79,86]。Perry等[77]利用Quickbird影像获取果林NDVI,根据NDVI与果林冠层总氮的统计关系反演了冠层氮含量; 曹淑静[78]基于GF-1影像数据分别建立了RVI,NDVI,EVI,VARI,NPCI和NRI共6种植被指数与苹果树冠层氮素之间的统计模型,发现以NPCI为自变量建立的二次多项式模型反演苹果树冠层氮素的精度和稳定性最高; 王凌[87]基于Landsat5和ALOS AVNIR-2A影像数据筛选出苹果树冠层、叶、花磷素的敏感光谱指数,建立了反演苹果林磷素的经验统计模型,发现基于Landsat5影像数据的模型反演精度更高。

从遥感反演方法来看,植被指数是经验统计模型的主要驱动因子,仅以不同波段光谱反射率(如红边波段)驱动的经验统计模型应用实例较少,一少部分学者进行了基于机理模型的苹果林(树)理化参数遥感反演研究,郭晓燕[82]采用基于HJ-1A HSI数据和实测数据的PROSAIL辐射传输模型反演了苹果树冠层叶绿素含量,实现了基于遥感数据与机理模型同化的叶绿素含量反演。然而目前这类以苹果林(树)理化参数为反演对象的机理模型无论其理论还是应用都十分缺乏。从遥感反演对象来看,当前的研究和应用基本关注于苹果林(树)LAI、叶绿素和养分,以生物量、类胡萝卜素、水分等为反演对象的研究鲜见。

1.2.2 果林环境参数反演

环境参数是影响果树生理生化表征的外部因素,常作为环境作用因子与生理生化参数一起描述果树生长状况。在苹果树栽培过程中,冠层温度、土壤水分和养分、光合有效辐射等是影响果树生长的主要环境参数[80,88⇓ -90],这些参数对揭示环境-植物相互作用下的果树长势、水分、养分和病虫害变化规律,提高果树监测准确性有重要意义。

现阶段,苹果林环境参数主要通过实地检测或实验室测样的方式获得[91-92],遥感反演参数的研究较少,已有的研究对象主要为冠层温度[80],该参数常用于反映果林蒸发蒸腾情况和水分亏损状况[93],由热红外遥感影像的比辐射率反演所得[80]。当前以其他苹果林环境参数为遥感反演对象的研究鲜见,而相关理论在其他植被区域的环境参数反演中已有了系统的研究[73,94 -95],可为应用于苹果林提供参考。其中,土壤水是果林的水分供应库,可由基于可见光—近红外、热红外和微波3类波段光谱特性开发的经验模型和机理模型进行反演[96],例如在植被缺水指数的构建模型中引入双层蒸散发模型,估算表层土壤的相对含水量[97],或将遥感反演的表层土壤水分同化到土壤-水-大气-植物(soil-water-atmosphere-plant,SWAP) 模型中估算根区土壤水分[94]等。土壤养分含量是决定果林潜在生产力的主要因素,可通过采用土壤表层反射光谱直接反演获得或通过果林生长状况的表现特征间接反演获得[98],例如建立土壤反射光谱与土壤有机质、全氮的回归预测模型,利用高光谱数据反演土壤养分含量[99],或利用作物模型与遥感数据同化的方法反演土壤养分含量[73]等。光合有效辐射是果林生长的能量来源,它控制着果树有效光合作用的速度,直接影响果树的发育、果实的产量和质量,可通过转换系数法、模型化参数法和查找表法等进行估算[100],例如采用基于卫星遥感数据的辐射传输模型估算云量下的太阳辐射总量,然后根据Alados等[101]提出的ηQ统计关系估算光合有效辐射[95],或利用大气-地表辐射传输模型,以MODIS等大气产品为模型的驱动参数估算光合有效辐射[102-103]等。

当前,苹果林环境参数遥感反演研究不足,尚无法为果园精准种植管理提供环境信息服务,但现已积累了相当数量的田间试验成果[91-92]可为遥感反演技术的应用提供数据参考并支撑相关遥感产品的真实性检验。

1.3 果园种植管理支撑

苹果园是一个复杂的生态系统,其种植管理涉及果树栽培和环境整治,是一项信息化需求高、耗时耗力的综合性工作。传统果园种植管理的信息化程度较低、立体化监测体系不完善,导致苹果生产效率低下,同时造成灌溉水浪费、化肥滥用和病虫害传播等问题[15,21,43],阻碍了果树正常生产、增加了管理成本还污染了生态环境。遥感技术可以快速获取果园基础信息和果林生理生化参数、环境参数,提供空天一体化的数字信息用于监测果树长势和评估果林(树)水分、养分和病虫害等状况,为科学、高效地调配资源进行估产、灌溉、施肥和病虫害防治等工作提供决策参考和支撑[16,20,76,81]。

1.3.1 果树长势监测

果树长势是一个包含生长状况和变化趋势的综合信息[104],快速获取该信息有助于果园管理者尽早了解果树生理状态、总体把握果园农情。传统上依靠人工实地调查果树生长情况、依据个人经验判断果树健康状态和生产潜力的方式已经不适用于当前果园大规模集中种植管理的模式[86,105]。现阶段,已经有大量研究证明了遥感在植被长势监测方面具有的优势[106⇓-108],一些研究成果已经应用于农业精准种植管理中[21,109 -110]。

长势信息反映果树的生长问题和生产潜力,可用个体特征和群体特征描述,其中个体特征指果树本体的特性参数,包括树高、冠幅、叶片养分含量、叶片叶绿素含量、开花数量和挂果数量等。万祖毅[111] 利用无人机遥感提取果树高度、冠幅和株数信息,根据样本果树高度、冠幅与挂果数量之间的统计关系预测了果林产量,单株果树估产预测值与实际值间的R2为0.79; Apolo-Apolo等[46]利用无人机获取苹果树的挂果情况,以此评估果林的生产能力。群体特征指果林整体特性参数,包括LAI和生物量等[107,112]。Bai等[20]利用PlanetScope星座影像提取苹果林NDVI等时间序列植被指数,构建基于CASA(Carnegie-Ames-Stanford approach)模型和时间序列植被指数的耦合模型估算果林净初级生产力(net primary productivity,NPP)积累量,较好地评估了果林的生产潜力。由于遥感信息反映的是瞬时状况,缺乏对生长机理性和连续性的描述,而植被生长模型综合考虑环境因素和植被生理生化因素,可以连续模拟植被每日生长发育状况[113],因此利用数据同化技术将果树生长模型应用到长势遥感监测中将能更精准地获知果树生长状况。目前已有相当数量的学者根据苹果树在不同生育期的生理生态参数响应机理开展了果树生长模型研究[114⇓-116],邵主恩等[117]利用修订STICS(simulateur multidisciplinaire pour les cultures standard)模型结合田间试验模拟苹果树水碳氮平衡过程,较好地反演了果林产量、单果重和蒸散发等指标; 张丽娜等[118]采用修订WinEPIC(environmental policy integrated climate)模型,模拟苹果园“气候-土壤-果树-管理”系统,反演了不同种植密度果园的水分生产力; 邬定荣等[119]利用4种机理模型模拟苹果树花期受冻驱动和热驱动2个过程来预测花期,并优选出模拟效果最好的模型。

植被生长模型具有机理性强、计算精度高等优点,但模型参数获取难度大、参数标定代价高等缺点又限制了模型的发展和应用[17]。当前,苹果树生长模型模拟果树长势情况的研究主要为田间试验,模型参数的获取方法大部分为地面检测,模型应用和验证的地区范围相对较小[117-118]。遥感数据覆盖范围广、低成本兼具高分辨率、高精度的特点可以使苹果树生长模型更好地服务果树长势监测。然而目前基于遥感数据和苹果树生长模型同化的长势监测研究鲜见,需要进一步加强这方面的探索。

1.3.2 果树水分管理

不同物候期苹果树对灌溉水的需求存在明显差异[120],因此对果树各物候期进行适量的水分调配是保障果树健康生长的重要手段。在实际生产中,大量苹果园分布在半干旱地区,那里灌溉水供需矛盾突出[121],进行精准化的果树水分管理非常必要。

苹果树体对水分的生理生化响应一般出现得比水分胁迫早得多,通常会改变果树的光谱表征,在水分胁迫早期无法被肉眼观察到[6],但可以被卫星、航空遥感平台搭载的传感器感知。早在2008年,Acevedo-Opazo等[122]利用航空遥感获得的NDVI数据划分出果树受水分胁迫的区域,在此之前尚缺乏利用高空间分辨率遥感数据直接评估果树水分状况的应用研究。随后,国内外学者对苹果林(树)水分遥感监测进行了更加深入的研究,重点关注果林(树)蒸散情况,根据果林(树)光谱信息反演日、周、季果树蒸散量数据[6,123],监测果林(树)水分胁迫状况[124],并以此计算果林(树)需水量[16],为精准灌溉提供决策依据。植被蒸散作为果林和土壤、大气进行水分交换的重要过程[125],De La Fuente-Sáiz等[123]建立了地表能量平衡模型(mapping evapotranspiration at high resolution with internalized calibration,METIME)计算Landsat7 ETM+影像中苹果林的实际蒸散量,并以此提出了估算果园需水量的目标; Odi-Lara等[16]根据Landsat5 TM,Landsat7 ETM+,Landsat8 OLI影像计算SAVI,利用基于该指数的单层土壤水分平衡模型反演滴灌苹果园日基准和周基准的蒸散量并确定灌水量,结果表明该模型能够较好地估算苹果生长季的蒸散量和灌水量,特别是周基准的估算效果最好。相当数量的研究发现温度和植被指数也可以指示苹果树的水分状况,Gómez-Candón等[126]利用无人机热辐射数据发现水分胁迫下的树冠温度明显高于正常灌溉下的树冠温度; Virlet等[127]利用可见光、近红外和热红外遥感数据,计算地气温差和水分亏损指数(water deficit index,WDI),比较它们对果树水分胁迫响应的敏感性,发现地气温差对苹果树蒸发表现出很强的敏感性。

从遥感技术的应用方向来看,目前大多数报道主要利用苹果林间蒸散的遥感估算量来评估水分状况并进一步预警水分胁迫、确定灌水量,缺乏针对果树冠层水分含量的反演研究。而直接遥感果树冠层水分含量的方式可能更有利于果园管理者掌握果林(树)实际水分状况,同时尽早预防果树水分胁迫、节约生产用水,特别是在缺水条件下,维持或提高果树生产力[128]。除此之外,遥感技术还可结合地理信息系统等工具,根据果树密度等条件确定不同区域喷头数量和大小,划定最佳输水管线[129],以此提高果园水分管理效益。

1.3.3 果树养分管理

苹果树体内约有70余种化学元素,其中生长发育必需的营养元素有16种,诸如氮、磷、钾、钙、镁等元素的缺乏或过量轻则造成果树生长延迟,重则导致果树绝产、病死[130],因此对苹果树进行养分监测和精准施肥是果园“保收增收”的必要手段。

传统的果树养分诊断方式一般需要损伤树体采样,或根据专家经验判断,这些方法不利于大面积推广使用[74]。果树光谱响应养分状况变化的表征可通过光谱仪感知,朱西存等[131-132]、李丙智等[133]和邢东兴等[134]利用光谱仪观测果树,发现苹果叶、花的氮、磷、钾等养分状况与光谱反射率存在相关关系,建立了较高精度的果树氮、磷、钾含量反演模型,证明遥感技术具有应用于苹果树养分动态监测的巨大潜力[135]。已有的研究发现果树叶片为果实提供了大量矿质营养元素,果实的产量和质量受叶片矿质元素影响[136],因此评价多年生果树营养状况的有效手段是冠层养分遥感监测[76],相关研究已在1.2.1节做了介绍,此处不再赘述。遥感技术能满足果树营养状况监测与评价的需求,相关成果可用在果林施肥方案的精准修正,实现快速、经济、便捷地确定追肥施用量[74]。另外遥感技术还可用于监测土壤养分含量[73,99],已在1.2.2节进行了说明,为果林测土配方施肥提供数据支撑,进一步促进果林“按需供肥”、农业“减肥”[74]。

在当前研究中,苹果林养分遥感监测的对象绝大多数为氮、磷、钾,主要采用经验统计法评估养分状况,基于物理机理的研究不足,且鲜有针对其他必需营养元素的监测研究,缺乏遥感监测技术指导苹果林精准施肥的探索。

1.3.4 果树病虫害管理

病虫害通常会对苹果生产造成严重的负面影响,在无法得到有效控制的情况下,病虫害轻则导致果树落叶、落果,影响苹果产量和品质,重则破坏树体、毁灭果林,造成大范围灾情流行[137]。因此,高效、及时地获取苹果树病虫害信息并尽早做出防治措施是有效阻止病虫害蔓延、减少果园损失的基本策略。

现有的研究已经证明了苹果树受病虫害胁迫后会出现自身光谱特征变化的现象[138-139],Delalieux等[140]发现1 350~1 750 nm和2 200~2 500 nm波段是区分苹果树黑星病胁迫的最重要光谱区域; Krezhova等[141]发现感染茎沟病毒苹果叶片的光谱反射率曲线向较短波长方向偏移。鉴于遥感获取光谱信息具有效率高、范围广的优势,Riom等[142]在1979年利用遥感手段对植被病虫害进行了研究。在实际生产中,由于病虫害种类较多[143],损害植被色素、水分、形态、结构的情况各不同[144],导致苹果树相应的光谱响应也具有多样性[8,105],因此提取并经过形式化表达的病虫害光谱响应特征是果树病虫害光学遥感监测的基本依据[143]。针对不同种类病虫害,目前主要采用可见光和近红外波段光谱信息进行识别监测工作,相关方法可分为2类: ①基于植被指数的病虫害遥感监测,Skoneczny等[8]利用反射光谱和花青素反射指数(nitrogen reflectance index,ARI)、复归一化差值植被指数(renormalized difference vegetation index,RDVI)和NRI对苹果树火疫病进行了检测,发现1 450 nm波段识别病害胁迫的效果最佳,ARI与胁迫程度的有较高相关性; Chandel等[29]利用无人机高空间分辨率可见光和多光谱成像技术识别患有白粉病的苹果树,发现8种植被指数可用于果树白粉病监测; Zeggada等[81]引入新的植被指数叶片发育指数(leaf development index,LDI)改进了果树赤霉病感染风险计算模型(a-scab model),使利用无人机遥感数据评估苹果树赤霉病的精度更高。 ②基于光谱响应特征的病虫害遥感监测,邢东兴[145]发现苹果树红蜘蛛虫害的胁迫程度在684 nm和762 nm波段处识别效果最好,黄叶病的胁迫程度则在603 nm和764 nm处识别效果最好,果树反射波谱的“红边”位置随病虫害的加重而发生“蓝移”。

现阶段,大部分苹果树病虫害遥感监测研究采用的是基于植被指数的方法,针对光谱响应特征的遥感监测研究较少,缺少以苹果树病虫害为监测对象的应用实例,并且相关研究以探寻新方法为主要目标,尚缺乏基于遥感技术的苹果树病虫害监测系统或服务平台的报道。

2 当前研究和应用存在的问题与展望

2.1 存在的问题

随着农业精准管理的呼吁和实践不断加强,苹果种植对果园生产支撑技术提出了更高的要求,遥感技术作为农业精准种植管理的信息支撑技术,已在果园基础信息调查、果林参数反演和果园种植管理支撑中展现出了监测效率高、空间覆盖广、结果精度好的优势。一些在其他农业实践中已有了许多成功经验的遥感监测方法同样在苹果园精准种植管理中拥有巨大的应用潜力。但就已有的报道和实践情况来看,目前还存在着以下问题:

1)机理性研究较少,部分应用领域研究不足。现有的遥感监测实例缺少对苹果树生长机理或果树-环境的物质-能量交换机理的应用,大多数研究主要采用经验统计模型建立目标参数与苹果树生长状况之间的关系,虽然原理简单、参数易获取且呈现地区性高精度,但没有明确的物理机制,难以描述果树的生长过程,区域外推的普适性较差。从遥感技术在苹果种植管理环节的应用情况来看,当前基于遥感技术的林龄和果园基础设施分布调查、环境参数反演、病虫害监测等领域的研究尚不足。

2)多技术集成化程度不高。现阶段综合利用遥感技术和其他新兴技术的研究与应用较少,立体监测体系不完善,不同技术间的集成化程度不高,支撑苹果生产管理的工作范围有限,较难平衡效率和成本,抬高了技术的应用和推广门槛。

3)缺乏大范围的示范应用。当前研究和应用多是在较小区域(一个或数个苹果园区)开展,关注于方法和可行性的探究,缺乏对技术大规模应用的成本问题和技术协同问题的考量。特别是针对遥感反演模型参数增多虽能提高精度但又会带来参数获取、模型运行成本高等问题,目前尚没有示范应用在两者权衡中寻找平衡点以满足村镇或中小型农场农户的需求。

2.2 研究和应用展望

在苹果园精准种植管理的需求下,遥感技术的应用将会更加注重精确和高效,相关技术的研究与开发也会偏向于用户端。特别随着遥感分辨率和重访频率的相关技术不断突破,卫星和航空遥感平台将会构成高-低空动态监测网,为果园精准种植管理提供高时间、空间、光谱和辐射分辨率的多元数据产品,并表现出以下的研究和应用趋势:

1)基于遥感数据的苹果树生长模拟机理模型研究和应用将会加强。果树生长模拟模型机理性强、区域外推的普适性较好,对果树生长状况有较好的反演效果,但模型参数获取难度大、参数标定代价高又制约了模型发展,而拥有大空间尺度、低成本和较高分辨率优势的遥感数据将能很好地解决制约机理模型应用的问题。为了适应大范围苹果园精准监测的需求,基于遥感数据的苹果树生长模拟机理模型相关研究和应用势必会加强。

2)遥感技术将与人工智能、物联网等新兴技术建成一体化苹果种植管理支撑系统。随着对地观测技术的发展,遥感数据产品的种类、数量不断增多,如何在海量数据中挖掘可用信息并高效应用于果树水分、养分、病虫害等监测工作和灌溉、施肥、运输等生产管理工作是提高遥感技术服务质量的关键。下一步亟需构建一体化的苹果种植管理支撑系统,结合专家系统和决策服务平台开发用户终端,提供高集成度的苹果生产支撑服务,亲和用户使用。

3)基于卫星数据的单木监测将成为大空间尺度区域内苹果园精准种植管理的主要支撑手段。果树单木管理是提高苹果园种植管理精准化的重要途径,目前已有大量基于航空遥感的苹果树单木监测研究支撑果园单木管理。相比于航空遥感,卫星遥感获取影像的空间尺度更大,可以实现更广范围的遥感监测,但是目前常用的大部分免费卫星影像如Landsat,Sentinel和高分系列的空间分辨率相对较低,无法提取出苹果树单木,因此尚不能用于单木监测。未来,随着遥感高空间分辨率技术不断突破,基于卫星数据的单木监测将可以实现推广,支撑县级、市级、省级、国家级尺度的苹果树单木管理。

4)果园种植信息遥感监测产品将综合多学科知识以满足苹果生产参与者的多元需求。苹果生产环节实际涉及多个行业大量参与者,果园种植信息是这些参与者进行决策的重要参考,例如果农需要根据监测信息制定树苗采购计划、采取灌溉、施肥等管理措施,树苗、肥料、农药等批发商需要根据监测信息评估市场需求,保险公司需要根据监测信息估算赔付,投资商需要根据监测信息规划投资。未来的果园种植信息遥感监测产品将综合农学、投资学、经济学、保险学等学科知识,使不同用户提前获得苹果园优势信息集,从而避免由于信息落后或信息不对称造成的损失,提高经济效益。

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Knowing the stand age of rubber tree (Hevea brasiliensis) plantations is vitally important for best management practices, estimations of rubber latex yields, and carbon cycle studies (e.g., biomass, carbon pools, and fluxes). However, the stand age (as estimated from the establishment year of rubber plantation) is not available across large regions. In this study, we analyzed Landsat time series images from 1987–2015 and developed algorithms to identify (1) the establishment year of rubber plantations; and (2) the pre-conversion land cover types, such as old rubber plantations, evergreen forests, and cropland. Exposed soil during plantation establishment and linear increases in canopy closure during non-production periods (rubber seedling to mature plantation) were used to identify the establishment year of rubber plantations. Based on the rubber plantation map for 2015 (overall accuracy = 97%), and 1981 Landsat images since 1987, we mapped the establishment year of rubber plantations on Hainan Island (R2 = 0.85/0.99, and RMSE = 2.34/0.54 years at pixel/plantation scale). The results show that: (1) significant conversion of croplands and old rubber plantations to new rubber plantations has occurred substantially in the northwest and northern regions of Hainan Island since 2000, while old rubber plantations were mainly distributed in the southeastern inland strip; (2) the pattern of rubber plantation expansion since 1987 consisted of fragmented plantations from smallholders, and there was no tendency to expand towards a higher altitude and steep slope regions; (3) the largest land source for new rubber plantations since 1988 was old rubber plantations (1.26 × 105 ha), followed by cropland (0.95 × 105 ha), and evergreen forests (0.68 × 105 ha). The resultant algorithms and maps of establishment year and pre-conversion land cover types are likely to be useful in plantation management, and ecological assessments of rubber plantation expansion in China.

陈媛媛, 游炯, 幸泽峰, 等.

世界主要国家精准农业发展概况及对中国的发展建议

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Review of precision agriculture development situations in the main countries in the world and suggestions for China

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程志强, 蒙继华.

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Bai X, Li Z, Li W, et al.

Comparison of machine-learning and CASA models for predicting apple fruit yields from time-series planet imageries

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Apple (Malus domestica Borkh. cv. “Fuji”), an important cash crop, is widely consumed around the world. Accurately predicting preharvest apple fruit yields is critical for planting policy making and agricultural management. This study attempted to explore an effective approach for predicting apple fruit yields based on time-series remote sensing data. In this study, time-series vegetation indices (VIs) were derived from Planet images and analyzed to further construct an accumulated VI (∑VIs)-based random forest (RF∑VI) model and a Carnegie–Ames–Stanford approach (CASA) model for predicting apple fruit yields. The results showed that (1) ∑NDVI was the optimal predictor to construct an RF model for apple fruit yield, and the R2, RMSE, and RPD values of the RF∑NDVI model reached 0.71, 16.40 kg/tree, and 1.83, respectively. (2) The maximum light use efficiency was determined to be 0.499 g C/MJ, and the CASASR model (R2 = 0.57, RMSE = 19.61 kg/tree, and RPD = 1.53) performed better than the CASANDVI model and the CASAAverage model (R2, RMSE, and RPD = 0.56, 24.47 kg/tree, 1.22 and 0.57, 20.82 kg/tree, 1.44, respectively). (3) This study compared the yield prediction accuracies obtained by the models using the same dataset, and the RF∑NDVI model (RPD = 1.83) showed a better performance in predicting apple fruit yields than the CASASR model (RPD = 1.53). The results obtained from this study indicated the potential of the RF∑NDVI model based on time-series Planet images to accurately predict apple fruit yields. The models could provide spatial and quantitative information of apple fruit yield, which would be valuable for agronomists to predict regional apple production to inform and develop national planting policies, agricultural management, and export strategies.

饶晓燕, 吴建伟, 李春朋, 等.

智慧苹果园“空-天-地”一体化监控系统设计与研究

[J]. 中国农业科技导报, 2021, 23(6):59-66.

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Rao X Y, Wu J W, Li C P, et al.

Design and research on “space-air-ground” integrated monitoring system for intelligent orchard

[J]. Journal of Agricultural Science and Technology, 2021, 23(6):59-66.

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In view of the problems in traditional apple orchard like imperfect data monitoring system, lack of scientific data for management decision and so on, this paper explored a kind of apple orchard “space-air-ground” integrated monitoring system, which integrated modern information technologies and intelligent equipment technologies such as satellite remote sensing(RS), unmanned aerial vehicle(UAV), agricultural internet of things(IOT), artificial intelligence(AI), etc., and integrated orchard information collection equipment suite, and built orchard data center based on SSM framework (Spring MVC, Spring, Mybatis). Through the integration and innovation of the new generation information technology collection system and the application of AI based image recognition of apple diseases and insect pests，the three-dimensional monitoring service function covering orchard soil, ecological environment, individual and group of fruit trees were realized, the efficiency and reliability of apple orchard monitoring were improved. It was of great significance of new form of business to promote the apple orchard production management to scientific, digital and intelligent.

刘海启.

以精准农业驱动农业现代化加速现代农业数字化转型

[J]. 中国农业资源与区划, 2019, 40(1):1-6,73.

[本文引用: 1]

Liu H Q.

Accelerating the digital transformation of modern agriculture by driving the agricultural modernization with precision agriculture

[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2019, 40(1):1-6,73.

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Bargoti S, Underwood J P.

Image segmentation for fruit detection and yield estimation in apple orchards

[J]. Journal of Field Robotics, 2017, 34(6):1039-1060.

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Ampatzidis Y, Partel V.

UAV-based high throughput phenotyping in citrus utilizing multispectral imaging and artificial intelligence

[J]. Remote Sensing, 2019, 11(4):410.

DOI:10.3390/rs11040410      URL     [本文引用: 1]

Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.

张宏鸣, 张国良, 朱珊娜, 等.

基于U-Net的葡萄种植区遥感识别方法

[J]. 农业机械学报, 2022, 53(4):173-182.

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Zhang H M, Zhang G L, Zhu S N, et al.

Remote sensing recognition method of grape planting regions based on U-Net

[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(4):173-182.

[本文引用: 1]

王凌, 赵庚星, 朱西存, 等.

6S辐射校正与像元分解结合提高苹果树花期冠层反射率反演精度

[J]. 农业工程学报, 2012, 28(9):96-102.

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Wang L, Zhao G X, Zhu X C, et al.

Improving retrieval accuracy of apple tree canopy reflectance at blossom stage by combining 6S radiometric correction with pixel unmixing method

[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(9):96-102.

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王爱. 黄土高原苹果树识别与蒸散发过程模拟[D]. 杨凌: 西北农林科技大学, 2020.

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Wang A. Apple tree identification and evapotranspiration process simulation on the Loess Plateau[D]. Yangling: Northwest Agriculture and Forestry University, 2020.

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辛群荣. 基于多时相高分影像的山区苹果园地信息提取研究[D]. 淄博: 山东理工大学, 2017.

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Xin Q R. Extracting montanic apple orchard information based on multi-temporal high resolution remote sensing image[D]. Zibo: Shandong University of Technology, 2017.

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Chandel A K, Khot L R, Sallato B C.

Apple powdery mildew infestation detection and mapping using high-resolution visible and multispectral aerial imaging technique

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Senthilnath J, Dokania A, Kandukuri M, et al.

Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV

[J]. Biosystems Engineering, 2016, 146:16-32.

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Martin M E, Newman S D, Aber J D, et al.

Determining forest species composition using high spectral resolution remote sensing data

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郭逸飞, 吴田军, 骆剑承, 等.

基于不确定性迭代优化的山地植被遥感制图

[J]. 地球信息科学学报, 2022, 24(7):1406-1419.

DOI:10.12082/dqxxkx.2022.210594      [本文引用: 1]

山地因其较高的异质性和特殊的环境特征给遥感科学及其应用带来了诸多问题和挑战。为实现山地植被信息的精准提取,本研究选择部分滇西北山地区域作为研究区开展方法实验,利用高分辨率遥感影像数据和数字高程模型,结合分区分层感知思想,提出一种基于不确定性理论的山地植被型组分类制图方法。首先结合地形对研究区影像进行多尺度分割制作图斑;然后根据图斑特征使用随机森林方法进行分类,将分类结果与对应类别样本间的相似性作为优化目标, 并构建混合熵模型定量计算图斑推测类型的不确定性,据此进行针对性的样本补充和分类模型的迭代优化。实验总体分类精度达90.8%,较迭代前提升了29.4%,Kappa系数达到0.875。在高不确定性区域,该方法相比使用一次性补样和随机补样方法的分类结果,精度分别提高了17%和13%。研究结果表明,通过人机交互的方式,基于不确定性理论为样本库融入增量信息的迭代优化方法能够有效提高植被型组分类的精度,相较于传统的样本选择方法具有更高的效率和更低的不确定性。

Guo Y F, Wu T J, Luo J C, et al.

Remote sensing mapping of mountain vegetation via uncertainty-based iterative optimization

[J]. Journal of Geo-Information Science, 2022, 24(7):1406-1419.

[本文引用: 1]

刘见礼, 廖小罕, 倪文俭, 等.

顾及单木三维形态的无人机立体影像单木识别算法

[J]. 地球信息科学学报, 2021, 23(10):1861-1872.

DOI:10.12082/dqxxkx.2021.210117      [本文引用: 1]

单木参数对当前的森林资源管理、生态研究以及生物多样性保护等具有重要意义。无人机立体影像数据与单木识别算法为单木参数的低成本、自动化获取提供了基础。现有研究表明,常用的基于局部最大值搜索的单木识别算法面对密集林分时存在严重的漏识别问题,影响了参数提取的精度,因此本文提出了顾及单木三维形态的无人机立体影像单木识别新算法。算法首先综合利用无人机立体影像的高程与RGB光谱信息,通过随机森林分类进行林冠区的提取;然后利用形态学的多层腐蚀、膨胀与连通区标记进行树冠相连单木的分离与树冠中心点的提取,从而实现单木自动化识别。本文选取内蒙古大兴安岭林区和四川王朗林区的4块样地进行验证,以目视解译数据为参考,分别与基于高程值的局部最大值搜索算法（算法A）、基于RGB光谱亮度值的局部最大值搜索算法（算法B）进行比较。结果显示：本文提出的算法在4个样地的平均F1-score为94.17%,与算法A和算法B相比分别提高了15.85%和9.37%;而对于密集样地,本文提出的算法在查全率上相比算法A和算法B分别提高51.79%和35.64%。结果表明本文提出的算法在不同林区均能够实现较好的单木识别效果,特别是能够有效避免密集林分下的漏识别问题,为基于无人机立体影像的单木识别研究提供了一种新的思路。

Liu J L, Liao X H, Ni W J, et al.

Individual tree recognition algorithm of UAV stereo imagery considering three-dimensional morphology of tree

[J]. Journal of Geo-Information Science, 2021, 23(10):1861-1872.

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颜伟, 周雯, 易利龙, 等.

森林类型遥感分类及变化监测研究进展

[J]. 遥感技术与应用, 2019, 34(3):445-454.

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Yan W, Zhou W, Yi L L, et al.

Research progress of remote sensing classification and change monitoring on forest types

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刘佳岐. 基于Landsat8遥感影像的扶风县苹果园地信息提取研究[D]. 杨凌: 西北农林科技大学, 2015.

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Liu J Q. Research on apple orchards information extraction of Fufeng County based on Landsat8 remote sensing image[D]. Yangling: Northwest Agriculture and Forestry University, 2015.

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郑利娟. 基于高分一/六号卫星影像特征的农作物分类研究[D]. 北京: 中国科学院大学(中国科学院遥感与数字地球研究所), 2017.

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Zheng L J. Crop classification using multi-features of Chinese Gaofen-1/6 satelite remote sensing images[D]. Beijing: University of Chinese Academy of Sciences (Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences), 2017.

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陈日强, 李长春, 杨贵军, 等.

无人机机载激光雷达提取果树单木树冠信息

[J]. 农业工程学报, 2020, 36(22):50-59.

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Chen R Q, Li C C, Yang G J, et al.

Extraction of crown information from individual fruit tree by UAV LiDAR

[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(22):50-59.

[本文引用: 2]

Solano F, Di Fazio S, Modica G.

A methodology based on GEOBIA and WorldView-3 imagery to derive vegetation indices at tree crown detail in olive orchards

[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 83:101912.

DOI:10.1016/j.jag.2019.101912      URL     [本文引用: 1]

胡林, 周国民, 丘耘, 等.

苹果树图像分割算法研究综述

[J]. 中国农业科技导报, 2015, 17(2):100-108.

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Hu L, Zhou G M, Qiu Y, et al.

Review on studying image segment algorithms of apple trees

[J]. Journal of Agricultural Science and Technology, 2015, 17(2):100-108.

DOI:10.13304/j.nykjdb.2014.498      [本文引用: 1]

Based on the studies about the application of image processing and computer vision on apple trees, the paper summarized the research progress made in the image segmentation algorithms for apple tree images. According to the technical system of image segmentation, this paper analyzed the main progress made in studying the apple tree image segmentation; systematically summarized the image segmentation algorithms and characteristics of apple tree application. The paper also drew an evaluation map for apple tree image segmentation algorithms, and prospected the development trend of studying apple tree image segmentation algorithms. All those provided references for further studies on the apple informatization theory and technology.

Wannasiri W, Nagai M, Honda K, et al.

Extraction of mangrove biophysical parameters using airborne LiDAR

[J]. Remote Sensing, 2013, 5(4):1787-1808.

DOI:10.3390/rs5041787      URL     [本文引用: 1]

Ok A O, Ozdarici-Ok A.

2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models

[J]. International Journal of Digital Earth, 2018, 11(6):583-608.

DOI:10.1080/17538947.2017.1337820      URL     [本文引用: 1]

Wu J, Yang G, Yang H, et al.

Extracting apple tree crown information from remote imagery using deep learning

[J]. Computers and Electronics in Agriculture, 2020, 174:105504.

DOI:10.1016/j.compag.2020.105504      URL     [本文引用: 1]

Sun G, Wang X, Yang H, et al.

A Canopy information measurement method for modern standardized apple orchards based on UAV multimodal information

[J]. Sensors, 2020, 20(10):2985.

DOI:10.3390/s20102985      URL     [本文引用: 2]

To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor UAV was used to collect canopy images in the apple orchard, and three-dimensional (3D) point-cloud models and vegetation index images of the orchard were generated with Pix4Dmapper software. A row and column detection method based on grayscale projection in orchard index images (RCGP) is proposed. Morphological information measurements of fruit tree canopies based on 3D point-cloud models are established, and a yield prediction model for fruit trees based on the UAV multimodal information is derived. The results are as follows: (1) When the ground sampling distance (GSD) was 2.13–6.69 cm/px, the accuracy of row detection in the orchard using the RCGP method was 100.00%. (2) With RCGP, the average accuracy of column detection based on grayscale images of the normalized green (NG) index was 98.71–100.00%. The hand-measured values of H, SXOY, and V of the fruit tree canopy were compared with those obtained with the UAV. The results showed that the coefficient of determination R2 was the most significant, which was 0.94, 0.94, and 0.91, respectively, and the relative average deviation (RADavg) was minimal, which was 1.72%, 4.33%, and 7.90%, respectively, when the GSD was 2.13 cm/px. Yield prediction was modeled by the back-propagation artificial neural network prediction model using the color and textural characteristic values of fruit tree vegetation indices and the morphological characteristic values of point-cloud models. The R2 value between the predicted yield values and the measured values was 0.83–0.88, and the RAD value was 8.05–9.76%. These results show that the UAV-based canopy information measurement method in apple orchards proposed in this study can be applied to the remote evaluation of canopy 3D morphological information and can yield information about modern standardized orchards, thereby improving the level of orchard informatization. This method is thus valuable for the production management of modern standardized orchards.

代佳佳.

基于高分与多时相中分影像的苹果园地提取

[J]. 中国农业资源与区划, 2021, 43(8):140-148.

[本文引用: 1]

Dai J J.

Apple orchard extraction based on high resolution images and multi-temporal midresolution images

[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2021, 43(8):140-148.

[本文引用: 1]

De La Fuente-Saiz D, Ortega-Farias S, Fonseca D, et al.

Calibration of METRIC model to estimate energy balance over a drip-irrigated apple orchard

[J]. Remote Sensing, 2017, 9(7):670.

DOI:10.3390/rs9070670      URL     [本文引用: 4]

Apolo-Apolo O, Perez-Ruiz M, Martinez-Guanter J, et al.

A cloud-based environment for generating yield estimation maps from apple orchards using UAV imagery and a deep learning technique

[J]. Frontiers in Plant Science, 2020, 11:1086.

DOI:10.3389/fpls.2020.01086      PMID:32765566      [本文引用: 2]

Farmers require accurate yield estimates, since they are key to predicting the volume of stock needed at supermarkets and to organizing harvesting operations. In many cases, the yield is visually estimated by the crop producer, but this approach is not accurate or time efficient. This study presents a rapid sensing and yield estimation scheme using off-the-shelf aerial imagery and deep learning. A Region-Convolutional Neural Network was trained to detect and count the number of apple fruit on individual trees located on the orthomosaic built from images taken by the unmanned aerial vehicle (UAV). The results obtained with the proposed approach were compared with apple counts made by an agrotechnician, and an value of 0.86 was acquired (MAE: 10.35 and RMSE: 13.56). As only parts of the tree fruits were visible in the top-view images, linear regression was used to estimate the number of total apples on each tree. An value of 0.80 (MAE: 128.56 and RMSE: 130.56) was obtained. With the number of fruits detected and tree coordinates two shapefile using Python script in Google Colab were generated. With the previous information two yield maps were displayed: one with information per tree and another with information per tree row. We are confident that these results will help to maximize the crop producers' outputs optimized orchard management.Copyright © 2020 Apolo-Apolo, Pérez-Ruiz, Martínez-Guanter and Valente.

Dong X, Zhang Z, Yu R, et al.

Extraction of information about individual trees from high-spatial-resolution UAV-acquired images of an orchard

[J]. Remote Sensing, 2020, 12(1):133-153.

DOI:10.3390/rs12010133      URL     [本文引用: 1]

The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer’s Accuracy (PA) and User’s Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m2 and 0.48 m2 were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees.

邵砾群, 侯建昀, 刘军弟, 等.

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The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.

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【目的】去除无人机多光谱遥感影像中的阴影,以提高苹果树冠层氮素含量反演模型精度。【方法】以山东省栖霞市苹果园为试验区,利用2019年6月采集的无人机多光谱影像,分别基于归一化阴影指数（normalized shaded vegetation index,NSVI）和归一化冠层阴影指数（normalized difference canopy shadow index,NDCSI）去除果树冠层多光谱影像中的阴影,提取非阴影区域果树冠层光谱信息;通过相关性分析方法,将基于原始光谱影像和基于NSVI、NDCSI去除阴影后提取的光谱数据与实测叶片氮素含量进行相关性分析,分别筛选氮素含量的敏感波段并构建光谱参量;采用偏最小二乘（partial least square,PLS）及支持向量机（support vector machine,SVM）方法构建果树冠层氮素含量反演模型并进行精度检验。【结果】绿光波段和红光波段为果树冠层氮素含量反演的敏感波段;阴影削弱了果树冠层的光谱信息,去除阴影前后,冠层多光谱各波段光谱差异较大,在红边波段及近红外波段尤为明显;基于2个阴影指数去除阴影后构建的氮素反演模型精度均有提升,最优模型为基于NDCSI去除阴影后构建的支持向量机氮素含量反演模型,该模型建模集R<sup>2</sup>和RPD分别为0.774、1.828;验证集R<sup>2</sup>和RPD分别为0.723、1.819。【结论】基于NDCSI可有效去除无人机多光谱果树冠层影像中的阴影,提高氮素含量反演精度,为果园氮素精准管理提供了有效参考。

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Remote sensing inversion of nitrogen content in apple canopy based on shadow removal in UAV multi-spectral remote sensing images

[J]. Scientia Agricultura Sinica, 2021, 54(10):2084-2094.

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【Objective】The shadows in UAV multi-spectral remote sensing images were removed to improve the accuracy of the nitrogen inversion model for apple canopy. 【Method】Using the UAV multi-spectral images collected in June 2019 at the apple orchard of Qixia city in Shandong province, as the experimental area, normalized shaded vegetation index (NSVI) and normalized canopy shadow index (NDCSI) were respectively used to remove shadow and to extract the spectral information of the canopy in non shadow area. The correlation analysis method was used to analyze the correlation between the spectral data, including the data obtained based on the original spectral images and the images after removing the shadow based on NSVI and NDCSI, and the measured leaf nitrogen content data, respectively, and then the sensitive wavelength of nitrogen content were screened and spectral parameters were constructed. Partial least squares (PLS) and support vector machine (SVM) methods were used to build the inversion model of nitrogen content and to carry out the precision inspection in the fruit tree canopy. 【Result】The results showed that the green band and red band were sensitive bands for the inversion of nitrogen content in fruit tree canopy based on UAV multi-spectral images. The spectral information of fruit tree canopy was weakened by shadow, and the spectral difference of canopy multispectral bands before and after shadow removal was significant, especially in red-edge band and near-infrared band. The accuracy of nitrogen inversion model based on two shadow indexes after shadow removal was improved, and the optimal model was the support vector machine nitrogen content inversion model based on NDCSI, the modeling set of this model R2 and RPD was 0.774 and 1.828, the validation set R2 and RPD were 0.723 and 1.819 respectively. 【Conclusion】NDCSI could effectively remove the shadow in the multi-spectral fruit tree canopy image of the UAV to improve remote sensing inversion accuracy of nitrogen content in apple canopy, so as to provide a useful reference for precise nitrogen management in orchard.

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